This package provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, feature selection and pathway analysis.
A web version, WebSpecmine, is now available, in case you do not have much skills in the R environment or programming, with a easy-to-use interface, with the features implemented in this package and a public repository with metabolomics data (you can also save your own data, private or not, if you create an account). The specmine R package may be better to use, if wanted a more flexible use of the provided features and use it with other R tools.So that the package can be fully used and installed correctly, some R packages have to be installed before installing specmine
install.packages(c('lattice', 'ggplot2', 'caret', 'BradleyTerry2', 'e1071', 'earth',
'fastICA','gam', 'ipred', 'kernlab', 'klaR', 'MASS', 'ellipse', 'mda',
'mgcv', 'mlbench','MLmetrics', 'nnet', 'party', 'pls', 'pROC', 'proxy',
'randomForest', 'RANN','spls', 'subselect', 'pamr', 'superpc', 'Cubist',
'testthat', 'igraph','Rweka', 'stats', 'scatterplot3d', 'compare',
'hyperSpec', 'ChemoSpec', 'baseline','rgl', 'Metrics', 'GGally',
'ggdendro', 'pcaPP', 'RColorBrewer', 'grid', 'methods', 'qdap',
'shinydashboard', 'shinyBS', 'shinyjs', 'DT', 'RMySQL', 'bcrypt',
'openssl', 'GGally','shinyWidgets', 'colourpicker', 'devtools',
'MLmetrics', 'speaq'))1: If bioconductor is not yet installed:
source("https://bioconductor.org/biocLite.R")
biocLite()2: Bioconductor packages necessary:
source("https://bioconductor.org/biocLite.R")
biocLite("impute", "genefilter", "xcms", "MAIT", "KEGGREST", "KEGGgraph", "mzR")devtools::install_github('cytoscape/r-cytoscape.js')install.packages("specmine")Note that, at some point, the development version may not be conpletely error free.
devtools::install_bitbucket('chrisbcl/metabolomicspackage', ref='master')Various types of data are supported, in many formats. The website considers that each data file represents one distinct sample, with exception for when one csv file of UV-VIS, IR and Raman Spectra is given and for the data file of concentrations data.
The peak lists data files must have the CSV format. Each CSV file must represent a sample and have two columns: the first one corresponds to the chemical shifts (in ppms) or the mass/charge ratios and the second one the intensities of those peaks.
Part of a CSV file example of a peak list:
ppm,intensity
0.74,0.0001
0.89,0.0004
0.90,0.0007
0.91,0.0005
0.91,0.0008
0.92,0.0004
0.94,0.0003
0.95,0.0004
0.96,0.0009
There are two nmr spectra formats that are supported.
The BRUKER format is supported, if the processed spectra are given. Each spectrum data has to be in a different folder. Each folder has to have the following structure:
At least the files procs and 1r have to be present. They have to be inside spectrumfoldername/pdata/1
The VARIAN format is supported, only if the raw fid file is given, alongside with the procpar file. Each spectrum data has to be in a different folder. Each folder has to have the following structure:
The MS spectral data files must either have .mzXML, .netCDF or mzData formats.
The data files of these type of spectra must be in one of the following formats: CSV, (J)DX, SPC or MS EXCEL (.xlsx).
For data in MS EXCEL or CSV files, each file must have two columns: the first one representing the wavenumber, wavelength or raman shift, according to the type of spectra, and the second one the value of the measurements.
When only one CSV file is given, the structure as to be similar to the following example (the first column corresponds to the wavenumber, wavelength or raman shift, according to the type of spectra):
,sampleName1,sampleName2
200,0.085956648,0.04830468
201,0.067182627,0.017316359
202,0.044842223,0.026930633
203,0.051335963,0.041539431
Concentrations data must be a CSV or TSV file with the samples names in the first column (each line then corresponds to a sample) and the concentrations values for each metabolite in the following columns. Alternatively, samples names can be in the first line (each column then corresponds to a sample) and the concentrations values for each metabolite in the following lines.
Part of a CSV example file of concentrations file:
Patient ID,1.6-Anhydro-beta-D-glucose,1-Methylnicotinamide,2-Aminobutyrate
PIF_178,40.85,65.37,18.73
PIF_087,62.18,340.36,24.29
PIF_090,270.43,64.72,12.18
NETL_005_V1,154.47,52.98,172.43
PIF_115,22.2,73.7,15.64
As regards to the metadata file, it can either have CSV or TSV format. Each line should correspond to a sample, where the first column represents the names of such samples, and the remaining ones the metadata classes.
The first column corresponds to the names of the samples. For the cases where more than one data file is given, the names of the samples have to correspond to the names of the data files.
Here you have an example of a metadata file:
Sample Name,Seasons
July2010,Winter
September2010,Spring
October2010,Spring
November2010,Spring
February2011,Sum/Aut
March2011,Sum/Aut
April2011,Sum/Aut
may2011,Sum/Aut
June2011,Winter
July2011,Winter
August2011,Winter
September2011,Spring
October2011,Spring
The data files used in the examples decribed in this section can be obtained in here.
A specmine dataset is a list with the following elements:
data: data frame of the data points. Each column corresponds to a samples and each line to a data variable. The values in each cell are the yy values of said variable in said sample;
metadata: data frame of the metadata information. Each column corresponds to a metadata variable and each line to a samples. The values in each cell are the values of said variable in said sample;
type: string indicating the type of data. It can either be “nmr-peaks”, “nmr-spectra”, “lcms-peaks”, “gcms-peaks”, “lcms-spectra”, “gcms-spectra”, “ir-spectra”, “uvv-spectra”, “raman-spectra”, “integrated-data”, “concentrations”, “undefined”;
description: a short description of the data;
labels: list with the following elements
x: xx axis labels;
val: yy axis labels.
1: Read NMR and MS Peak lists data into list of data samples
read_csvs_folder(foldername, header=TRUE, sep=“,”, dec=“.”, …)
foldername: string containing the path of the data folder;
header: boolean value (TRUE or FALSE) indicating whether data files have a header row with the names of the data variables. Defaults to TRUE;
sep: the separator character of the data values. Defaults to “,”;
dec: character used in the file for decimal points. Defaults to “.”;
…: additional parameters for read.csv function from utils package.
2: Read metadata file (optional step but recomended)
read_metadata(filename, header.col = T, header.row = T, sep = “,”)
filename : string indicating the path of the file with the metadata;
header.col: boolean value (TRUE or FALSE) indicating if the metadata CSV file contains a header column with the name of the metadata variables. Defaults to TRUE;
header.row: boolean value (TRUE or FALSE) indicating if the metadata CSV file contains a header row with the name of the samples. Defaults to TRUE;
sep: the separator character. Defaults to “,”.
3: Perform Peak Alignment, into specmine dataset
group_peaks(sample.list, type, method = “own”, metadata = NULL, samp.classes = 1, description = “”, label.x = NULL, label.values = NULL, step = 0.03)
sample.list: list containing the sample’s data. This list can be obtained from the function read_csvs_folder above;
type: type of the data. Can either be “nmr-peaks”, “lcms-peaks” or “gcms-peaks”;
method: method of peak alignment. Can either be
“own”: Specmine method. Default value;
“metaboanalyst”: MetaboAnalyst method, which is for using the peak alignment used in MetaboAnalyst software.
metadata: data frame containing the metadata. Can be obtained from the function read_metadata above; optional but recomended
samp.classes: string containg the metadata’s variable name to be used in the MetaboAnalyst method. Can be obtained from colnames(metadata_dataframe). Defaults to the variable represented by the first column.
description: short description of the data. optional
label.x: the label for the x values. optional
label.values: the label for the y values. optional
step: step value for the peak alignment process in the specmine method. Defaults to 0.03.
1. Strings indicating where data and metadata is:
nmr_peaks_lists_data_folder="/home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data"
nmr_peaks_lists_metadata_file="/home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/propolis_nmr_metadata.csv"2. Read data folder:
nmr_peaks_list=read_csvs_folder(nmr_peaks_lists_data_folder)## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/AC_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/AC_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/AC_sp.CSV"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/AC_wi.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/AN_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/AN_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/AN_sp.CSV"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/AN_wi.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/BR_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/BR_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/BR_sp.CSV"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/BR_wi.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/CE_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/CE_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/CE_sp.CSV"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/CE_wi.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/CN_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/CN_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/CN_sp.CSV"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/CN_wi.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/DC_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/DC_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/DC_sp.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/DC_wi.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/FP_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/FP_sp.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/FP_wi.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/IT_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/IT_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/IT_sp.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/JB_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/JB_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/JB_sp.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/JB_wi.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/PU_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/PU_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/PU_sp.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/PU_wi.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/SA_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/SA_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/SA_sp.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/SA_wi.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/SJ_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/SJC_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/SJC_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/SJC_sp.CSV"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/SJ_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/SJ_sp.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/SJ_wi.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/UR_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/UR_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/UR_sp.CSV"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/UR_wi.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/VR_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/VR_sm.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/VR_sp.CSV"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/XX_au.csv"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/XX_sm.CSV"
## [1] "Reading sample /home/scardoso/Documents/metabolomics_datasets/NMR/Peak lists/propolis/Propolis_NMR Data_data/XX_wi.csv"
3. Read metadata file:
nmr_peaks_metadata=read_metadata(nmr_peaks_lists_metadata_file)4. Align peaks using specmine method with a step of 0.03 (default values, so it is not necessary to define them), which will now return the specmine dataset:
nmr_peaks_dataset=group_peaks(nmr_peaks_list, "nmr-peaks", metadata = nmr_peaks_metadata, description="propolis nmr samples", label.x = "ppm", label.values = "intensity")5. Table of the data just loaded (it is possible to realize that there are missing values):
DT::datatable(nmr_peaks_dataset$data, options=list(scrollX = TRUE))6. Table of the metadata just loaded:
DT::datatable(nmr_peaks_dataset$metadata)read_Bruker_files(bruker_directory, metadata_file=NULL, m.header_col=T, m.header_row=T, m.sep=“,”, samples.names=NULL, zipped=T, description=“”, label.x=“ppm”, label.values=“intensity”)
bruker_directory: string containing the path of the data folder with all the spectra folders;
metadata_file: string containing the path of the metadata file; optional but recomended
m.header_col: boolean value (TRUE or FALSE) indicating if the metadata file contains a header column with the name of the metadata variables. Defaults to TRUE;
m.header_row: boolean value (TRUE or FALSE) indicating if the metadata file contains a header row with the name of the samples. Defaults to TRUE;
m.sep: the separator character of the metadata file. Defaults to “,”;
samples.names: CSV file where the first column represents the samples names and in the second column the names of the spectra directories to which they correspond. If NULL, it will be considered that the directories names are the samples names (it has to be the same names that appear in the metadata file);
zipped: Boleean value (TRUE or FALSE) indicating if the spectra directories are zipped (.zip) or not. The compressed files must have the extension .zip. If not, you will have to uncompress them yourself.
description: a short text describing the dataset. optional
label.x: the label for the x values. optional
label.values: the label for the y values. optional
TO USE THIS FUNCTION YOU MUST HAVE PYTHON3 INSTALLED, WITH THE MODULE nmrglue INSTALLED
read_varian_spectra_raw(varian_spectra_directory, metadata_file=NULL, m.header_col=T, m.header_row=T, m.sep=“,”, samples.names=NULL, zero_filling=T, apodization=T, zipped=T, description=“”, label.x=“ppm”, label.values=“intensity”)
varian_spectra_directory: string containing the path of the data folder with all the spectra folders;
metadata_file: string containing the path of the metadata file; optional but recomended
m.header_col: boolean value (TRUE or FALSE) indicating if the metadata file contains a header column with the name of the metadata variables. Defaults to TRUE;
m.header_row: boolean value (TRUE or FALSE) indicating if the metadata file contains a header row with the name of the samples. Defaults to TRUE;
m.sep: the separator character of the metadata file. Defaults to “,”;
samples.names: CSV file where the first column represents the samples names and in the second column the names of the spectra directories to which they correspond. If NULL, it will be considered that the directories names are the samples names (it has to be the same names that appear in the metadata file);
zero_filling: boolean value (TRUE or FALSE) indicating whether zero-filling should be performed or not when processing the fid spectra. Defaults to TRUE;
apodization: boolean value (TRUE or FALSE) indicating whether apodization should be performed or not when processing the fid spectra. Defaults to TRUE;
zipped: Boleean value (TRUE or FALSE) indicating if the spectra directories are zipped (.zip) or not. The compressed files must have the extension .zip. If not, you will have to uncompress them yourself.
description: a short text describing the dataset. optional
label.x: the label for the x values. optional
label.values: the label for the y values. optional
Detection of peaks, followed by alignment of those peaks
detect_nmr_peaks_from_dataset(dataset, baseline_tresh=50000, ap.method=“own”, ap.samp.classes=1, ap.step=0.03)
dataset: a specmine dataset of type nmr-spectra;
baseline_tresh: Minimum intensity value that peaks must have. Peaks with intensity smaller than baseline_tresh will not be considered as detected peaks. Defaults to 50000;
ap.method: method of peak alignment. Can either be
“own”: Specmine method. Default value;
“metaboanalyst”: MetaboAnalyst method, which is for using the peak alignment used in MetaboAnalyst software.
ap.samp.classes: string containg the metadata’s variable name to be used in the MetaboAnalyst method. Can be obtained from colnames(metadata_dataframe). Defaults to the variable represented by the first column.
ap.step: step value for the peak alignment process in the specmine method. Defaults to 0.03.
The example data here used was obtained from the Metabolights database, under the ID MTBLS151. The data was obtained using the get_metabolights_study function.
1. Strings indicating where data and metadata is:
bruker_nmr_spectra_folder="/home/scardoso/Documents/metabolomics_datasets/NMR/Spectra/MTBLS151"
bruker_nmr_metadata_file="/home/scardoso/Documents/metabolomics_datasets/NMR/Spectra/MTBLS151/metadata.csv"2. Loading NMR bruker spectral data to specmine:
In this data, the spectra folders’ names correspond to the names of the samples, so no file will be given to the argument samples.names. These folders are zipped (.zip)
nmr_bruker_spectra_dataset=read_Bruker_files(bruker_nmr_spectra_folder, metadata_file=bruker_nmr_metadata_file)## Reading Metadata file
## Reading sample L-IL308C1-1 in /home/scardoso/temp/L-IL308C1-1/L-IL308C1-1/10/pdata/1
## Reading sample L-IL308C1-2 in /home/scardoso/temp/L-IL308C1-2/L-IL308C1-2/10/pdata/1
## Reading sample L-IL308C1-3 in /home/scardoso/temp/L-IL308C1-3/L-IL308C1-3/10/pdata/1
## Reading sample L-IL308C2-1 in /home/scardoso/temp/L-IL308C2-1/L-IL308C2-1/10/pdata/1
## Reading sample L-IL308C2-2 in /home/scardoso/temp/L-IL308C2-2/L-IL308C2-2/10/pdata/1
## Reading sample L-IL308C2-3 in /home/scardoso/temp/L-IL308C2-3/L-IL308C2-3/10/pdata/1
## Reading sample L-IL308C3-1 in /home/scardoso/temp/L-IL308C3-1/L-IL308C3-1/10/pdata/1
## Reading sample L-IL308C3-2 in /home/scardoso/temp/L-IL308C3-2/L-IL308C3-2/10/pdata/1
## Reading sample L-IL308C3-3 in /home/scardoso/temp/L-IL308C3-3/L-IL308C3-3/10/pdata/1
## Reading sample L-IL308C4-1 in /home/scardoso/temp/L-IL308C4-1/L-IL308C4-1/10/pdata/1
## Reading sample L-IL308C4-2 in /home/scardoso/temp/L-IL308C4-2/L-IL308C4-2/10/pdata/1
## Reading sample L-IL308C4-3 in /home/scardoso/temp/L-IL308C4-3/L-IL308C4-3/10/pdata/1
## Reading sample L-IL308C8-1 in /home/scardoso/temp/L-IL308C8-1/L-IL308C8-1/10/pdata/1
## Reading sample L-IL308C8-2 in /home/scardoso/temp/L-IL308C8-2/L-IL308C8-2/10/pdata/1
## Reading sample L-IL308C8-3 in /home/scardoso/temp/L-IL308C8-3/L-IL308C8-3/10/pdata/1
## Reading sample L-IL308T1-1 in /home/scardoso/temp/L-IL308T1-1/L-IL308T1-1/10/pdata/1
## Reading sample L-IL308T1-2 in /home/scardoso/temp/L-IL308T1-2/L-IL308T1-2/10/pdata/1
## Reading sample L-IL308T1-3 in /home/scardoso/temp/L-IL308T1-3/L-IL308T1-3/10/pdata/1
## Reading sample L-IL308T2-1 in /home/scardoso/temp/L-IL308T2-1/L-IL308T2-1/10/pdata/1
## Reading sample L-IL308T2-2 in /home/scardoso/temp/L-IL308T2-2/L-IL308T2-2/10/pdata/1
## Reading sample L-IL308T2-3 in /home/scardoso/temp/L-IL308T2-3/L-IL308T2-3/10/pdata/1
## Reading sample L-IL308T3-1 in /home/scardoso/temp/L-IL308T3-1/L-IL308T3-1/10/pdata/1
## Reading sample L-IL308T3-2 in /home/scardoso/temp/L-IL308T3-2/L-IL308T3-2/10/pdata/1
## Reading sample L-IL308T3-3 in /home/scardoso/temp/L-IL308T3-3/L-IL308T3-3/10/pdata/1
## Reading sample L-IL308T4-1 in /home/scardoso/temp/L-IL308T4-1/L-IL308T4-1/10/pdata/1
## Reading sample L-IL308T4-2 in /home/scardoso/temp/L-IL308T4-2/L-IL308T4-2/10/pdata/1
## Reading sample L-IL308T4-3 in /home/scardoso/temp/L-IL308T4-3/L-IL308T4-3/10/pdata/1
## Reading sample L-IL308T8-1 in /home/scardoso/temp/L-IL308T8-1/L-IL308T8-1/10/pdata/1
## Reading sample L-IL308T8-2 in /home/scardoso/temp/L-IL308T8-2/L-IL308T8-2/10/pdata/1
## Reading sample L-IL308T8-3 in /home/scardoso/temp/L-IL308T8-3/L-IL308T8-3/10/pdata/1
## Reading sample L-IL7C1-1 in /home/scardoso/temp/L-IL7C1-1/L-IL7C1-1/10/pdata/1
## Reading sample L-IL7C1-2 in /home/scardoso/temp/L-IL7C1-2/L-IL7C1-2/10/pdata/1
## Reading sample L-IL7C1-3 in /home/scardoso/temp/L-IL7C1-3/L-IL7C1-3/10/pdata/1
## Reading sample L-IL7C2-1 in /home/scardoso/temp/L-IL7C2-1/L-IL7C2-1/10/pdata/1
## Reading sample L-IL7C2-2 in /home/scardoso/temp/L-IL7C2-2/L-IL7C2-2/10/pdata/1
## Reading sample L-IL7C2-3 in /home/scardoso/temp/L-IL7C2-3/L-IL7C2-3/10/pdata/1
## Reading sample L-IL7C3-1 in /home/scardoso/temp/L-IL7C3-1/L-IL7C3-1/10/pdata/1
## Reading sample L-IL7C3-2 in /home/scardoso/temp/L-IL7C3-2/L-IL7C3-2/10/pdata/1
## Reading sample L-IL7C3-3 in /home/scardoso/temp/L-IL7C3-3/L-IL7C3-3/10/pdata/1
## Reading sample L-IL7C4-1 in /home/scardoso/temp/L-IL7C4-1/L-IL7C4-1/10/pdata/1
## Reading sample L-IL7C4-2 in /home/scardoso/temp/L-IL7C4-2/L-IL7C4-2/10/pdata/1
## Reading sample L-IL7C4-3 in /home/scardoso/temp/L-IL7C4-3/L-IL7C4-3/11/pdata/1
## Reading sample L-IL7C8-1 in /home/scardoso/temp/L-IL7C8-1/L-IL7C8-1/10/pdata/1
## Reading sample L-IL7C8-2 in /home/scardoso/temp/L-IL7C8-2/L-IL7C8-2/10/pdata/1
## Reading sample L-IL7C8-3 in /home/scardoso/temp/L-IL7C8-3/L-IL7C8-3/10/pdata/1
## Reading sample L-IL7T1-1 in /home/scardoso/temp/L-IL7T1-1/L-IL7T1-1/10/pdata/1
## Reading sample L-IL7T1-2 in /home/scardoso/temp/L-IL7T1-2/L-IL7T1-2/10/pdata/1
## Reading sample L-IL7T1-3 in /home/scardoso/temp/L-IL7T1-3/L-IL7T1-3/10/pdata/1
## Reading sample L-IL7T2-1 in /home/scardoso/temp/L-IL7T2-1/L-IL7T2-1/10/pdata/1
## Reading sample L-IL7T2-2 in /home/scardoso/temp/L-IL7T2-2/L-IL7T2-2/10/pdata/1
## Reading sample L-IL7T2-3 in /home/scardoso/temp/L-IL7T2-3/L-IL7T2-3/10/pdata/1
## Reading sample L-IL7T3-1 in /home/scardoso/temp/L-IL7T3-1/L-IL7T3-1/10/pdata/1
## Reading sample L-IL7T3-1 in /home/scardoso/temp/L-IL7T3-1/L-IL7T3-1/20/pdata/1
## Reading sample L-IL7T3-2 in /home/scardoso/temp/L-IL7T3-2/L-IL7T3-2/10/pdata/1
## Reading sample L-IL7T3-3 in /home/scardoso/temp/L-IL7T3-3/L-IL7T3-3/10/pdata/1
## Reading sample L-IL7T4-1 in /home/scardoso/temp/L-IL7T4-1/L-IL7T4-1/10/pdata/1
## Reading sample L-IL7T4-2 in /home/scardoso/temp/L-IL7T4-2/L-IL7T4-2/10/pdata/1
## Reading sample L-IL7T4-3 in /home/scardoso/temp/L-IL7T4-3/L-IL7T4-3/10/pdata/1
## Reading sample L-IL7T8-1 in /home/scardoso/temp/L-IL7T8-1/L-IL7T8-1/10/pdata/1
## Reading sample L-IL7T8-2 in /home/scardoso/temp/L-IL7T8-2/L-IL7T8-2/10/pdata/1
## Reading sample L-IL7T8-3 in /home/scardoso/temp/L-IL7T8-3/L-IL7T8-3/10/pdata/1
## Reading sample L-KDC1-1 in /home/scardoso/temp/L-KDC1-1/L-KDC1-1/10/pdata/1
## Reading sample L-KDC1-2 in /home/scardoso/temp/L-KDC1-2/L-KDC1-2/10/pdata/1
## Reading sample L-KDC1-3 in /home/scardoso/temp/L-KDC1-3/L-KDC1-3/10/pdata/1
## Reading sample L-KDC2-1 in /home/scardoso/temp/L-KDC2-1/L-KDC2-1/10/pdata/1
## Reading sample L-KDC2-2 in /home/scardoso/temp/L-KDC2-2/L-KDC2-2/10/pdata/1
## Reading sample L-KDC2-3 in /home/scardoso/temp/L-KDC2-3/L-KDC2-3/10/pdata/1
## Reading sample L-KDC3-1 in /home/scardoso/temp/L-KDC3-1/L-KDC3-1/10/pdata/1
## Reading sample L-KDC3-2 in /home/scardoso/temp/L-KDC3-2/L-KDC3-2/10/pdata/1
## Reading sample L-KDC3-3 in /home/scardoso/temp/L-KDC3-3/L-KDC3-3/10/pdata/1
## Reading sample L-KDC4-1 in /home/scardoso/temp/L-KDC4-1/L-KDC4-1/10/pdata/1
## Reading sample L-KDC4-2 in /home/scardoso/temp/L-KDC4-2/L-KDC4-2/10/pdata/1
## Bruker file does not exist in datapath, or other problems with bruker files...
## Reading sample L-KDC4-2 in /home/scardoso/temp/L-KDC4-2/L-KDC4-2/20/pdata/1
## Reading sample L-KDC4-3 in /home/scardoso/temp/L-KDC4-3/L-KDC4-3/10/pdata/1
## Reading sample L-KDC8-1 in /home/scardoso/temp/L-KDC8-1/L-KDC8-1/10/pdata/1
## Reading sample L-KDC8-2 in /home/scardoso/temp/L-KDC8-2/L-KDC8-2/10/pdata/1
## Reading sample L-KDC8-3 in /home/scardoso/temp/L-KDC8-3/L-KDC8-3/10/pdata/1
## Reading sample L-KDT1-1 in /home/scardoso/temp/L-KDT1-1/L-KDT1-1/10/pdata/1
## Reading sample L-KDT1-2 in /home/scardoso/temp/L-KDT1-2/L-KDT1-2/10/pdata/1
## Reading sample L-KDT1-3 in /home/scardoso/temp/L-KDT1-3/L-KDT1-3/10/pdata/1
## Reading sample L-KDT2-1 in /home/scardoso/temp/L-KDT2-1/L-KDT2-1/10/pdata/1
## Reading sample L-KDT2-2 in /home/scardoso/temp/L-KDT2-2/L-KDT2-2/10/pdata/1
## Reading sample L-KDT2-3 in /home/scardoso/temp/L-KDT2-3/L-KDT2-3/10/pdata/1
## Reading sample L-KDT3-1 in /home/scardoso/temp/L-KDT3-1/L-KDT3-1/10/pdata/1
## Reading sample L-KDT3-2 in /home/scardoso/temp/L-KDT3-2/L-KDT3-2/10/pdata/1
## Reading sample L-KDT3-3 in /home/scardoso/temp/L-KDT3-3/L-KDT3-3/10/pdata/1
## Reading sample L-KDT4-1 in /home/scardoso/temp/L-KDT4-1/L-KDT4-1/10/pdata/1
## Reading sample L-KDT4-2 in /home/scardoso/temp/L-KDT4-2/L-KDT4-2/10/pdata/1
## Reading sample L-KDT4-3 in /home/scardoso/temp/L-KDT4-3/L-KDT4-3/10/pdata/1
## Reading sample L-KDT8-1 in /home/scardoso/temp/L-KDT8-1/L-KDT8-1/10/pdata/1
## Reading sample L-KDT8-2 in /home/scardoso/temp/L-KDT8-2/L-KDT8-2/10/pdata/1
## Reading sample L-KDT8-3 in /home/scardoso/temp/L-KDT8-3/L-KDT8-3/10/pdata/1
## Reading sample R-IL308C1 in /home/scardoso/temp/R-IL308C1/R-IL308C1/10/pdata/1
## Reading sample R-IL308C2 in /home/scardoso/temp/R-IL308C2/R-IL308C2/10/pdata/1
## Reading sample R-IL308C3 in /home/scardoso/temp/R-IL308C3/R-IL308C3/10/pdata/1
## Reading sample R-IL308C4 in /home/scardoso/temp/R-IL308C4/R-IL308C4/10/pdata/1
## Reading sample R-IL308C8 in /home/scardoso/temp/R-IL308C8/R-IL308C8/10/pdata/1
## Reading sample R-IL308T1 in /home/scardoso/temp/R-IL308T1/R-IL308T1/10/pdata/1
## Reading sample R-IL308T2 in /home/scardoso/temp/R-IL308T2/R-IL308T2/10/pdata/1
## Reading sample R-IL308T3 in /home/scardoso/temp/R-IL308T3/R-IL308T3/10/pdata/1
## Reading sample R-IL308T4 in /home/scardoso/temp/R-IL308T4/R-IL308T4/10/pdata/1
## Reading sample R-IL308T8 in /home/scardoso/temp/R-IL308T8/R-IL308T8/10/pdata/1
## Reading sample R-IL7C1 in /home/scardoso/temp/R-IL7C1/R-IL7C1/10/pdata/1
## Reading sample R-IL7C2 in /home/scardoso/temp/R-IL7C2/R-IL7C2/10/pdata/1
## Reading sample R-IL7C3 in /home/scardoso/temp/R-IL7C3/R-IL7C3/10/pdata/1
## Reading sample R-IL7C4 in /home/scardoso/temp/R-IL7C4/R-IL7C4/10/pdata/1
## Reading sample R-IL7C8 in /home/scardoso/temp/R-IL7C8/R-IL7C8/10/pdata/1
## Reading sample R-IL7T1 in /home/scardoso/temp/R-IL7T1/R-IL7T1/10/pdata/1
## Reading sample R-IL7T2 in /home/scardoso/temp/R-IL7T2/R-IL7T2/10/pdata/1
## Reading sample R-IL7T3 in /home/scardoso/temp/R-IL7T3/R-IL7T3/10/pdata/1
## Reading sample R-IL7T4 in /home/scardoso/temp/R-IL7T4/R-IL7T4/10/pdata/1
## Reading sample R-IL7T8 in /home/scardoso/temp/R-IL7T8/R-IL7T8/10/pdata/1
## Reading sample R-KDC1 in /home/scardoso/temp/R-KDC1/R-KDC1/10/pdata/1
## Reading sample R-KDC2 in /home/scardoso/temp/R-KDC2/R-KDC2/10/pdata/1
## Reading sample R-KDC3 in /home/scardoso/temp/R-KDC3/R-KDC3/10/pdata/1
## Reading sample R-KDC4 in /home/scardoso/temp/R-KDC4/R-KDC4/10/pdata/1
## Reading sample R-KDC8 in /home/scardoso/temp/R-KDC8/R-KDC8/10/pdata/1
## Reading sample R-KDT1 in /home/scardoso/temp/R-KDT1/R-KDT1/10/pdata/1
## Reading sample R-KDT2 in /home/scardoso/temp/R-KDT2/R-KDT2/10/pdata/1
## Reading sample R-KDT3 in /home/scardoso/temp/R-KDT3/R-KDT3/10/pdata/1
## Reading sample R-KDT4 in /home/scardoso/temp/R-KDT4/R-KDT4/10/pdata/1
## Reading sample R-KDT8 in /home/scardoso/temp/R-KDT8/R-KDT8/10/pdata/1
## Creating dataset (this may take a while)
## Done.
3. Table of the data just loaded:
DT::datatable(nmr_bruker_spectra_dataset$data, options=list(scrollX = TRUE))## Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html